Evolução do grau de eficiência do mercado de moedas criptográficas de 2014 a 2020: uma análise baseada em seus componentes fractais
Palavras-chave:
Cryptocurrencies, Fractal Market Hypothesis, Adaptive Markets, Market EfficiencyResumo
Objetivo: Este estudo visa analisar a evolução da eficiência do mercado criptoativos com base em aspectos fractais da série histórica de preços de 15 criptomoedas e um índice de referência desenvolvido para este mercado (CRIX).
Metodologia: As análises propostas partem do índice de eficiência proposto por Kristoufek e Vosvrda (2013), que captura os vieses de memória de longo e curto prazo, bem como a autocorrelação de primeira ordem. O banco de dados cobre o período de 02/08/2014 a 31/12/2020. Usando a análise de quebra estrutural para séries temporais, foi possível dividir a amostra em cinco períodos de análise, e o índice de eficiência foi calculado para cada um deles.
Resultados: Foi identificada a existência de oscilações entre os índices de eficiência ao longo dos períodos analisados, verificando uma maior ineficiência em momentos de ascensão do mercado. Além disso, pode-se observar que, em geral, este mercado vem ganhando eficiência ao longo dos anos, embora ainda não tenha alcançado a ausência de ineficiência. Esta conclusão corrobora os estudos sobre a adaptação da eficiência do mercado com base em seus investidores e agentes. Finalmente, pode-se caracterizar o cenário atual como uma bolha especulativa, o que, devido à presença do efeito de manada, permite a existência de arbitragem.
Originalidade: Pesquisas nesta área ainda são recentes, pois se trata de um novo segmento financeiro, portanto existem várias dúvidas e lacunas na literatura. Neste sentido, a adoção de uma abordagem longitudinal para identificar a evolução da eficiência deste mercado não só é interessante como também é uma abordagem pouco explorada pela literatura.
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Referências
Barabási, A. L., Szépfalusy, P., & Vicsek, T. (1991). Multifractal spectra of multi-affine functions. Physica A: Statistical Mechanics and its Applications, 178(1), 17-28.
Białkowski, J. (2020). Cryptocurrencies in institutional investors’ portfolios: Evidence from industry stop-loss rules. Economics Letters, 191, 108834.
Bracciali, A., Grossi, D., & de Haan, R. (2021). Decentralization in open quorum systems: limitative results for Ripple and Stellar. In 2nd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2020). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.
Briére, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, tangible return: portfolio diversification with bitcoin. Journal of Asset Management, 16(6), 365-373.
Brito, J., Shadab, H. B., & Castillo O'Sullivan, A. (2014). Bitcoin financial regulation: securities, derivatives, prediction markets, and gambling. Columbia Science and Technology Law Review,16,144-221.
Cagli, E. C. (2019). Explosive behavior in the prices of Bitcoin and altcoins. Finance Research Letters, 29, 398-403.
Cajueiro, D. O. & Tabak, B. M. (2004). The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient. Physica A: Statistical Mechanics and its Applications, 336(3-4), 521-537.
Cajueiro, D. O., Tabak, B. M., & Andrade, R. F. (2009). Fluctuations in interbank network dynamics. Physical Review E, 79(3), 037101.
Calvet, L. E., & Fisher, A. J. (2013). Extreme risk and fractal regularity in finance. Contemporary Mathematics, 601, 65-94.
Caporale, G. M., Gil-Alana, L., Plastun, A., & Makarenko, I. (2016). Long memory in the Ukrainian stock market and financial crises. Journal of Economics and Finance, 40(2), 235-257.
Chan, G., Hall, P., & Poskitt, D. S. (1995). Periodogram-based estimators of fractal properties. The Annals of Statistics, 1684-1711.
Charfeddine, L., Benlagha, N., & Maouchi, Y. (2020). Investigating the dynamic relationship between cryptocurrencies and conventional assets: implications for financial investors. Economic Modelling, 85, 198-217.
Davies, S., & Hall, P. (1999). Fractal analysis of surface roughness by using spatial data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(1), 3-37.
Di Matteo, T., Aste, T., & Dacorogna, M. M. (2003). Scaling behaviors in differently developed markets. Physica A: Statistical Mechanics and its Applications, 324(1-2), 183-188.
Dubovikov, M. M., Starchenko, N. V., & Dubovikov, M. S. (2004). Dimension of the minimal cover and fractal analysis of time series. Physica A: Statistical Mechanics and its Applications, 339(3-4), 591-608.
Elliott, R. N. (1994). R. N. Elliott's Masterworks. Prechter, Robert R., Jr. (ed.) Gainesville, GA: New Classics Library. pp. 70, 217, 194, 196.
Fama, E. F. (1970). Efficient capital markets: a review of theory and empirical work. The Journal of Finance, 25(2), 383-417.
Fama, E. F. (1991). Efficient Capital Markets: II. The Journal of Finance,45 (5), 1575-1617.
Galbraith, J. K. (1994). A short history of financial euphoria. 1 ed. London: Penguin Books.
Genton, M. G. (1998). Highly robust variogram estimation. Mathematical Geology, 30(2), 213-221.
Gurdgiev, C., & O’Loughlin, D. (2020). Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty. Journal of Behavioral and Experimental Finance, 25, 100271.
Hall, P., & Wood, A. (1993). On the performance of box-counting estimators of fractal dimension. Biometrika, 80(1), 246-251.
Hasso, T., Pelster, M., & Breitmayer, B. (2019). Who trades cryptocurrencies, how do they trade it, and how do they perform? Evidence from brokerage accounts. Journal of Behavioral and Experimental Finance, 23, 64-74.
Hsieh, Y. Y., Vergne, J. P., & Wang, S. (2017). The internal and external governance of blockchain-based organizations: Evidence from cryptocurrencies. In: Campbell-Verduyn, M. (2017). Bitcoin and Beyond: Cryptocurrencies, Blockchains and Global Governance. New York, NY, pp. 48-68.
Jovanovic, F., & Schinckus, C. (2013). The emergence of econophysics: a new approach in modern financial theory. History of Political Economy, 45(3), 443-474.
Kahneman, D., & Tversky, A. (2013). Prospect theory: an analysis of decision under risk. In: MacLean, L. C., & Ziemba, W. T. (2013). Handbook of the fundamentals of financial decision making: Part I. World Scientific, 99-127.
Kimura, H. (2005). The financial market from the fractal optics perpsective. Revista de Administração de Empresas, 45(4), 124-125.
Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3, 3415.
Kristoufek, L., & Vosvrda, M. (2013). Measuring capital market efficiency: global and local correlations structure. Physica A: Statistical Mechanics and its Applications, 392(1), 184-193.
Kristoufek, L., & Vosvrda, M. (2019). Cryptocurrencies market efficiency ranking: not so straightforward. Physica A: Statistical Mechanics and its Applications, 531, 120853.
Lánský, J. (2017). Bitcoin system. Acta Informatica Pragensia, 6(1), 20-31.
Lo, A. W. (2004). The adaptive markets hypothesis. The Journal of Portfolio Management, 30(5), 15-29.
Lo, A. W. (2005). Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis. Journal of Investment Consulting, 7(2), 21-44.
Mandelbrot, B. (1963). New methods in statistical economics. Journal of Political Economy, 71(5), 421-440.
Mandelbrot, B. B. (2005). The inescapable need for fractal tools in finance. Annals of Finance, 1(2), 193-195.
Mantegna, R. N. (1991). Lévy walks and enhanced diffusion in Milan stock exchange. Physica A: Statistical Mechanics and its Applications, 179(2), 232-242.
Mantegna, R. N., & Kertész, J. (2011). Focus on statistical physics modeling in economics and finance. New Journal of Physics, 13(2), 025011.
Mnif, E., Jarboui, A., & Mouakhar, K. (2020). How the cryptocurrency market has performed during COVID 19? A multifractal analysis. Finance Research Letters, 36, 101647.
Mosteanu, N. R., & Faccia, A. (2021). Fintech Frontiers in Quantum Computing, Fractals, and Blockchain Distributed Ledger: Paradigm Shifts and Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 19.
Nakamoto, S. (2008). Bitcoin: a peer-to-peer electronixic cash system. Disponível em: https://bitcoin.org/bitcoin.pdf.
Nguyen, T. V. H., Nguyen, B. T., Nguyen, T. C., & Nguyen, Q. Q. (2019). Bitcoin return: impacts from the introduction of new altcoins. Research in International Business and Finance, 48(C), 420-425.
Patil, A. C., & Rastogi, S. (2020). Multifractal analysis of market efficiency across structural breaks: Implications for the adaptive market hypothesis. Journal of Risk and Financial Management, 13(10), 248.
Peng, C. K., Buldyrev, S. V., Goldberger, A. L., Havlin, S., Simons, M., & Stanley, H. E. (1993). Finite-size effects on long-range correlations: Implications for analyzing DNA sequences. Physical Review E, 47(5), 3730.
Peters, E. E. (1994) Fractal Market analysis: applying caos theory to investment and economics. New York: Willey.
Peters, E. E. (1996). Chaos and order in the capital markets: A new view of cycles, prices, and market volatility. New York, NY: John Wiley and Sons, Inc.
Reed, J. (2017). Litecoin: an Introduction to Litecoin cryptocurrency and Litecoin mining. North Charleston, United States.
Schinckus, C. (2011). What can econophysics contribute to financial economics? International Review of Economics, 58(2), 147-163.
Selmi, R., Tiwari, A., & Hammoudeh, S. (2018). Efficiency or speculation? A dynamic analysis of the Bitcoin market. Economics bulletin, 38(4), 2037-2046.
Serroukh, A., Walden, A. T., & Percival, D. B. (2000). Statistical properties and uses of the wavelet variance estimator for the scale analysis of time series. Journal of the American Statistical Association, 95(449), 184-196.
Silva, P. V. J. G., Klotzle, M. C., Pinto, A. C. F., & Gomes, L. L. (2019). Herding behavior and contagion in the cryptocurrency market. Journal of Behavioral and Experimental Finance, 22, 41-50.
Tran, V., & Leirvik, T. (2019). Efficiency in the markets of crypto-currencies. Finance Research Letters, 35, 101382.
Trimborn, S., & Härdle, W. K. (2018). CRIX an index for cryptocurrencies. Journal of Empirical Finance, 49, 107-122.
Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148(C), 80-82.
Urquhart, A., & McGroarty, F. (2014). Calendar effects, market conditions and the Adaptive Market Hypothesis: Evidence from long-run US data. International Review of Financial Analysis, 35, 154-166.
Wei, W. C. (2018). Liquidity and market efficiency in cryptocurrencies. Economics Letters, 168, 21-24.
Yermack, D. (2013). Is Bitcoin a real currency? An economic appraisal (No. w19747). National Bureau of Economic Research, 36(2), 843-850.
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